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Continuous
validation of data
quality through
automated quality checks
Automate data quality checks within the data pipelines through hooks, so that bad data does not reach production.
The Main Ingredients
Fully
automated
Best
practice
Production data
is protected
How it works?
Best Practices & Data Quality
Expose changes to consumers after quality has been assured with pre-merge hooks
Version control​
Create discoverable history of the data lake with an ordered set of versions, and ensure clear communication on which versions are used where
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How our customers are using
CI\CD for data
Reproducibility for ML experiments Read more>
Increasing research velocity with isolated environments Read more>
Dev Test environments for complex pipelines Read more>
